OpenRaiser/Intern-Atlas huggingface dataset:
Learning Co-segmentation by Segment Swapping for Retrieval and Discovery
Venue: arXiv cs.CV 2022
Year: 2022

The goal of this work is to efficiently identify visually similar patterns in images, e.g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart. Lack of training data is a key challenge for this co-segmentation task. We present a simple yet surprisingly effective approach to overcome this difficulty: we generate synthetic training pairs by selecting segments in an image and copy-pasting them into another image. We then learn to predict the repeated region masks. We find that it is crucial to predict the correspondences as an auxiliary task and to use Poisson blending and style transfer on the training pairs to generalize on real data. We analyse results with two deep architectures relevant to our joint image analysis task: a transformer-based architecture and Sparse Nc-Net, a recent network designed to predict coarse correspondences using 4D convolutions. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset and achieves competitive performance on two place recognition benchmarks, Tokyo247 and Pitts30K. We also demonstrate the potential of our approach for unsupervised image collection analysis by introducing a spectral graph clustering approach to object discovery and demonstrating it on the object discovery dataset of \cite{rubinstein2013unsupervised} and the Brueghel dataset. Our code and data are available at http://imagine.enpc.fr/~shenx/SegSwap/.
Dynamic Differential-Privacy Preserving SGD
Venue: arXiv cs.LG 2022
Year: 2022

The vanilla Differentially-Private Stochastic Gradient Descent (DP-SGD), including DP-Adam and other variants, ensures the privacy of training data by uniformly distributing privacy costs across training steps. The equivalent privacy costs controlled by maintaining the same gradient clipping thresholds and noise powers in each step result in unstable updates and a lower model accuracy when compared to the non-DP counterpart. In this paper, we propose the dynamic DP-SGD (along with dynamic DP-Adam, and others) to reduce the performance loss gap while maintaining privacy by dynamically adjusting clipping thresholds and noise powers while adhering to a total privacy budget constraint. Extensive experiments on a variety of deep learning tasks, including image classification, natural language processing, and federated learning, demonstrate that the proposed dynamic DP-SGD algorithm stabilizes updates and, as a result, significantly improves model accuracy in the strong privacy protection region when compared to the vanilla DP-SGD. We also conduct theoretical analysis to better understand the privacy-utility trade-off with dynamic DP-SGD, as well as to learn why Dynamic DP-SGD can outperform vanilla DP-SGD.
A Non-Deterministic Multiset Query Language
Venue: arXiv cs.LO 2022
Year: 2022

We develop a multiset query and update language executable in a term rewriting system. Its most remarkable feature, besides non-standard approach to quantification and introduction of fresh values, is non-determinism - a query result is not uniquely determined by the database. We argue that this feature is very useful, e.g., in modelling user choices during simulation or reachability analysis of a data-centric business process - the intended application of our work. Query evaluation is implemented by converting the query into a terminating term rewriting system and normalizing the initial term which encapsulates the current database. A normal form encapsulates a query result. We prove that our language can express any relational algebra query. Finally, we present a simple business process specification framework (and an example specification). Both syntax and semantics of our query language is implemented in Maude.
Twin-width VI: the lens of contraction sequences
Venue: arXiv cs.DS 2022
Year: 2022

A contraction sequence of a graph consists of iteratively merging two of its vertices until only one vertex remains. The recently introduced twin-width graph invariant is based on contraction sequences. More precisely, if one puts red edges between two vertices representing non-homogeneous subsets, the twin-width is the minimum integer $d$ such that a contraction sequence keeps red degree at most $d$. By changing the condition imposed on the trigraphs (i.e., graphs with some edges being red) and possibly slightly tweaking the notion of contractions, we show how to characterize the well-established bounded rank-width, tree-width, linear rank-width, path-width, and proper minor-closed classes by means of contraction sequences. As an application we give a transparent alternative proof of the celebrated Courcelle's theorem (actually of its generalization by Courcelle, Makowsky, and Rotics), that MSO$_2$ (resp. MSO$_1$) model checking on graphs with bounded tree-width (resp. bounded rank-width) is fixed-parameter tractable in the size of the input sentence. We then explore new avenues along the general theme of contraction sequences both in order to refine the landscape between bounded tree-width and bounded twin-width (via spanning twin-width) and to capture more general classes than bounded twin-width. To this end, we define an oriented version of twin-width, where appearing red edges are oriented away from the newly contracted vertex, and the mere red out-degree should remain bounded. Surprisingly, classes of bounded oriented twin-width coincide with those of bounded twin-width. Finally we examine, from an algorithmic standpoint, the concept of partial contraction sequences, where, instead of terminating on a single-vertex graph, the sequence ends when reaching a particular target class.
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
Venue: arXiv cs.LG 2022
Year: 2022

The strong demand for efficient and performant deployment of Deep Learning (DL) applications prompts the rapid development of a rich DL ecosystem. To keep up with this fast advancement, it is crucial for modern DL frameworks to efficiently integrate a variety of optimized tensor algebra libraries and runtimes as their backends and generate the fastest possible executable using these backends. However, current DL frameworks require significant manual effort and expertise to integrate every new backend while failing to unleash its full potential. Given the fast-evolving nature of the DL ecosystem, this manual approach often slows down continuous innovations across different layers; it prevents hardware vendors from the fast deployment of their cutting-edge libraries, DL framework developers must repeatedly adjust their hand-coded rules to accommodate new versions of libraries, and machine learning practitioners need to wait for the integration of new technologies and often encounter unsatisfactory performance. In this paper, we propose Collage, a DL framework that offers seamless integration of DL backends. Collage provides an expressive backend registration interface that allows users to precisely specify the capability of various backends. By leveraging the specifications of available backends, Collage automatically searches for an optimized backend placement strategy for a given workload and execution environment. Our evaluation shows that Collage outperforms the best existing framework for each hardware by $1.26\times$, $1.43\times$, $1.40\times$ on average on NVIDIA's RTX 2070 GPU, V100 GPU, and Intel's Xeon 8259CL CPU, respectively. Collage has been open-sourced and deployed in Apache TVM.
Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Recent years have witnessed growing interests in multimedia recommendation, which aims to predict whether a user will interact with an item with multimodal contents. Previous studies focus on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation. Firstly, only collaborative item-item relationships are implicitly modeled through high-order item-user-item co-occurrences. We argue that the latent semantic item-item structures underlying these multimodal contents could be beneficial for learning better item representations and assist the recommender models to comprehensively discover candidate items. Secondly, previous studies disregard the fine-grained multimodal fusion. Although having access to multiple modalities might allow us to capture rich information, we argue that the simple coarse-grained fusion by linear combination or concatenation in previous work is insufficient to fully understand content information and item relationships.To this end, we propose a latent structure MIning with ContRastive mOdality fusion method (MICRO for brevity). To be specific, we devise a novel modality-aware structure learning module, which learns item-item relationships for each modality. Based on the learned modality-aware latent item relationships, we perform graph convolutions that explicitly inject item affinities to modality-aware item representations. Then, we design a novel contrastive method to fuse multimodal features. These enriched item representations can be plugged into existing collaborative filtering methods to make more accurate recommendations. Extensive experiments on real-world datasets demonstrate the superiority of our method over state-of-the-art baselines.
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing
Venue: arXiv cs.LG 2022
Year: 2022

Recently, Graph Neural Networks (GNNs) have become state-of-the-art algorithms for analyzing non-euclidean graph data. However, to realize efficient GNN training is challenging, especially on large graphs. The reasons are many-folded: 1) GNN training incurs a substantial memory footprint. Full-batch training on large graphs even requires hundreds to thousands of gigabytes of memory. 2) GNN training involves both memory-intensive and computation-intensive operations, challenging current CPU/GPU platforms. 3) The irregularity of graphs can result in severe resource under-utilization and load-imbalance problems. This paper presents a GNNear accelerator to tackle these challenges. GNNear adopts a DIMM-based memory system to provide sufficient memory capacity. To match the heterogeneous nature of GNN training, we offload the memory-intensive Reduce operations to in-DIMM Near-Memory-Engines (NMEs), making full use of the high aggregated local bandwidth. We adopt a Centralized-Acceleration-Engine (CAE) to process the computation-intensive Update operations. We further propose several optimization strategies to deal with the irregularity of input graphs and improve GNNear's performance. Comprehensive evaluations on 16 GNN training tasks demonstrate that GNNear achieves 30.8$\times$/2.5$\times$ geomean speedup and 79.6$\times$/7.3$\times$(geomean) higher energy efficiency compared to Xeon E5-2698-v4 CPU and NVIDIA V100 GPU.
Robust Deep Learning from Crowds with Belief Propagation
Venue: arXiv cs.LG 2022
Year: 2022

Crowdsourcing systems enable us to collect large-scale dataset, but inherently suffer from noisy labels of low-paid workers. We address the inference and learning problems using such a crowdsourced dataset with noise. Due to the nature of sparsity in crowdsourcing, it is critical to exploit both probabilistic model to capture worker prior and neural network to extract task feature despite risks from wrong prior and overfitted feature in practice. We hence establish a neural-powered Bayesian framework, from which we devise deepMF and deepBP with different choice of variational approximation methods, mean field (MF) and belief propagation (BP), respectively. This provides a unified view of existing methods, which are special cases of deepMF with different priors. In addition, our empirical study suggests that deepBP is a new approach, which is more robust against wrong prior, feature overfitting and extreme workers thanks to the more sophisticated BP than MF.
Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we address the problem of forecasting the trajectory of an egocentric camera wearer (ego-person) in crowded spaces. The trajectory forecasting ability learned from the data of different camera wearers walking around in the real world can be transferred to assist visually impaired people in navigation, as well as to instill human navigation behaviours in mobile robots, enabling better human-robot interactions. To this end, a novel egocentric human trajectory forecasting dataset was constructed, containing real trajectories of people navigating in crowded spaces wearing a camera, as well as extracted rich contextual data. We extract and utilize three different modalities to forecast the trajectory of the camera wearer, i.e., his/her past trajectory, the past trajectories of nearby people, and the environment such as the scene semantics or the depth of the scene. A Transformer-based encoder-decoder neural network model, integrated with a novel cascaded cross-attention mechanism that fuses multiple modalities, has been designed to predict the future trajectory of the camera wearer. Extensive experiments have been conducted, with results showing that our model outperforms the state-of-the-art methods in egocentric human trajectory forecasting.
Dropout in Training Neural Networks: Flatness of Solution and Noise Structure
Venue: arXiv cs.LG 2022
Year: 2022

It is important to understand how the popular regularization method dropout helps the neural network training find a good generalization solution. In this work, we show that the training with dropout finds the neural network with a flatter minimum compared with standard gradient descent training. We further find that the variance of a noise induced by the dropout is larger at the sharper direction of the loss landscape and the Hessian of the loss landscape at the found minima aligns with the noise covariance matrix by experiments on various datasets, i.e., MNIST, CIFAR-10, CIFAR-100 and Multi30k, and various structures, i.e., fully-connected networks, large residual convolutional networks and transformer. For networks with piece-wise linear activation function and the dropout is only at the last hidden layer, we then theoretically derive the Hessian and the covariance of dropout randomness, where these two quantities are very similar. This similarity may be a key reason accounting for the goodness of dropout.
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
Venue: arXiv cs.CL 2022
Year: 2022

Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations shared within the diabetes online community from a causality perspective. Materials and Methods: More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect-tweet dataset was manually labeled and used to train 1) a fine-tuned Bertweet model to detect causal sentences containing a causal association 2) a CRF model with BERT based features to extract possible cause-effect associations. Causes and effects were clustered in a semi-supervised approach and visualised in an interactive cause-effect-network. Results: Causal sentences were detected with a recall of 68% in an imbalanced dataset. A CRF model with BERT based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68%. This led to 96,676 sentences with cause-effect associations. "Diabetes" was identified as the central cluster followed by "Death" and "Insulin". Insulin pricing related causes were frequently associated with "Death". Conclusions: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multi-word cause and corresponding effect as expressed in diabetes-related tweets leveraging BERT-based architectures and visualised as cause-effect-network. Extracting causal associations on real-life, patient reported outcomes in social media data provides a useful complementary source of information in diabetes research.
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions
Venue: arXiv cs.LG 2022
Year: 2022

People affected by machine learning model decisions may benefit greatly from access to recourses, i.e. suggestions about what features they could change to receive a more favorable decision from the model. Current approaches try to optimize for the cost incurred by users when adopting a recourse, but they assume that all users share the same cost function. This is an unrealistic assumption because users might have diverse preferences about their willingness to change certain features. In this work, we introduce a new method for identifying recourse sets for users which does not assume that users' preferences are known in advance. We propose an objective function, Expected Minimum Cost (EMC), based on two key ideas: (1) when presenting a set of options to a user, there only needs to be one low-cost solution that the user could adopt; (2) when we do not know the user's true cost function, we can approximately optimize for user satisfaction by first sampling plausible cost functions from a distribution, then finding a recourse set that achieves a good cost for these samples. We optimize EMC with a novel discrete optimization algorithm, Cost Optimized Local Search (COLS), which is guaranteed to improve the recourse set quality over iterations. Experimental evaluation on popular real-world datasets with simulated users demonstrates that our method satisfies up to 25.89 percentage points more users compared to strong baseline methods, while, the human evaluation shows that our recourses are preferred more than twice as often as the strongest baseline recourses. Finally, using standard fairness metrics we show that our method can provide more fair solutions across demographic groups than baselines. We provide our source code at: https://github.com/prateeky2806/EMC-COLS-recourse
Callee: Recovering Call Graphs for Binaries with Transfer and Contrastive Learning
Venue: arXiv cs.SE 2022
Year: 2022

Recovering binary programs' call graphs is crucial for inter-procedural analysis tasks and applications based on them.transfer One of the core challenges is recognizing targets of indirect calls (i.e., indirect callees). Existing solutions all have high false positives and negatives, making call graphs inaccurate. In this paper, we propose a new solution Callee combining transfer learning and contrastive learning. The key insight is that, deep neural networks (DNNs) can automatically identify patterns concerning indirect calls, which can be more efficient than designing approximation algorithms or heuristic rules to handle various cases. Inspired by the advances in question-answering applications, we utilize contrastive learning to answer the callsite-callee question. However, one of the toughest challenges is that DNNs need large datasets to achieve high performance, while collecting large-scale indirect-call ground-truths can be computational-expensive. Since direct calls and indirect calls share similar calling conventions, it is possible to transfer knowledge learned from direct calls to indirect ones. Therefore, we leverage transfer learning to pre-train DNNs with easy-to-collect direct calls and further fine-tune the indirect-call DNNs. We evaluate Callee on several groups of targets, and results show that our solution could match callsites to callees with an F1-Measure of 94.6%, much better than state-of-the-art solutions. Further, we apply Callee to binary code similarity detection and hybrid fuzzing, and found it could greatly improve their performance.
ArchABM: an agent-based simulator of human interaction with the built environment. $CO_2$ and viral load analysis for indoor air quality
Venue: arXiv cs.MA 2022
Year: 2022

Recent evidence suggests that SARS-CoV-2, which is the virus causing a global pandemic in 2020, is predominantly transmitted via airborne aerosols in indoor environments. This calls for novel strategies when assessing and controlling a building's indoor air quality (IAQ). IAQ can generally be controlled by ventilation and/or policies to regulate human-building-interaction. However, in a building, occupants use rooms in different ways, and it may not be obvious which measure or combination of measures leads to a cost- and energy-effective solution ensuring good IAQ across the entire building. Therefore, in this article, we introduce a novel agent-based simulator, ArchABM, designed to assist in creating new or adapt existing buildings by estimating adequate room sizes, ventilation parameters and testing the effect of policies while taking into account IAQ as a result of complex human-building interaction patterns. A recently published aerosol model was adapted to calculate time-dependent carbon dioxide ($CO_2$) and virus quanta concentrations in each room and inhaled $CO_2$ and virus quanta for each occupant over a day as a measure of physiological response. ArchABM is flexible regarding the aerosol model and the building layout due to its modular architecture, which allows implementing further models, any number and size of rooms, agents, and actions reflecting human-building interaction patterns. We present a use case based on a real floor plan and working schedules adopted in our research center. This study demonstrates how advanced simulation tools can contribute to improving IAQ across a building, thereby ensuring a healthy indoor environment.
Equivalent Versions of Total Flow Analysis
Venue: arXiv cs.NI 2022
Year: 2022

Total Flow Analysis (TFA) is a method for conducting the worst-case analysis of time sensitive networks without cyclic dependencies. In networks with cyclic dependencies, Fixed-Point TFA introduces artificial cuts, analyses the resulting cycle-free network with TFA, and iterates. If it converges, it does provide valid performance bounds. We show that the choice of the specific cuts used by Fixed-Point TFA does not affect its convergence nor the obtained performance bounds, and that it can be replaced by an alternative algorithm that does not use any cut at all, while still applying to cyclic dependencies.